Detecting Missing Translations in Neural Machine Translation Using Information Quantity in Sentences

Autor:	Jing Bai, Hiroyuki Shinnou, Wen Ma, Shin Fujii, Rui Cao
Rok vydání:	2019
Předmět:	Information quantity Machine translation business.industry Computer science Artificial intelligence computer.software_genre business computer Natural language processing Sentence Word (computer architecture)
Zdroj:	TAAI
DOI:	10.1109/taai48200.2019.8959850
Popis:	This study attempts a method based on the information quantity in the source sentence, for detecting missing translations in neural machine translation. It assumes the necessary information quantity for the sentence as a whole, based on the word information quantity in the source sentence and, in the same way, detects missing translations by making a comparison with the presumed target sentence information quantity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2233ea9be5bbd25465f0deb7cfe2f6f6 https://doi.org/10.1109/taai48200.2019.8959850 Zobrazit plný text záznamu